The PEG-PCL-PEG Hydrogel as an Implanted Ophthalmic Delivery System after Glaucoma Filtration Surgery; a Pilot Study

Currently, filtration surgery has been considered as the most effective therapy for glaucoma; however, the scar formation in the surgical area may often lead to failure to the procedure. An implanted drug delivery system may provide localized and sustained release of a drug over an extended period. Poly (ethylene glycol)-poly (ε-caprolactone)-poly (ethylene glycol) (PEG-PCL-PEG, PECE) hydrogel has been successfully synthesized and determined as thermosensitive and biocompatible. In order to overcome the limitations of common local ophthalmic medications, we investigated the function of a self-assembled PECE hydrogel as an intracameral injection-implanted drug carrier to inhibit the formation of postoperative scarring. Following intraoperative administration bevacizumab-loaded hydrogel intracameral was injected into rabbit eyes; the status of the bleb and filtration fistula formed following the filtering surgery were also examined through pathologic evaluation. Due to the sustained release of bevacizumab from the hydrogel, neovascularization and scar formation were inhibited; moreover, there were no corneal abnormalities and other ocular tissue damage found in the rabbits. This suggests that the PECE hydrogel may be considered as the novel biomaterial with potential as a sustained release system in glaucoma filtering surgery. Further studies require in shedding the light on the subject

A Limited Memory BFGS Method for Solving Large-Scale Symmetric Nonlinear Equations

A limited memory BFGS (L-BFGS) algorithm is presented for solving large-scale symmetric nonlinear equations, where a line search technique without derivative information is used. The global convergence of the proposed algorithm is established under some suitable conditions. Numerical results show that the given method is competitive to those of the normal BFGS methods

Exploring the prognostic value of S100A11 and its association with immune infiltration in breast cancer

Abstract Breast cancer (BC) is a severe danger to women’s lives and health globally. S100A11 is aberrantly expressed in many carcinomas and serves a crucial function in cancer development. However, the role of S100A11 in BC is unclear. In this study, we utilized multiple databases and online tools, including the TCGA database, cBioPortal, and STRING, to evaluate the significance of S100A11 in BC prognosis and immune infiltration. We found that S100A11 was considerably more abundant in BC tissues. Survival analysis indicated that individuals with S100A11 high expression of BC had shorter overall survival. Multivariate Cox regression analysis revealed that high S100A11 expression independently influenced the poor outcome of patients with BC (HR = 1.738, 95%CI 1.197–2.524). Our nomogram incorporating five factors, including S100A11, age, clinical stage, N, and M, was developed to anticipate the survival probability in BC prognosis. The model demonstrated good consistency and accuracy. Furthermore, the mutation rete of S100A11 was 14%. Survival analysis suggested that breast cancer patients with S100A11 mutation had a worse prognosis. KEGG pathway enrichment analysis revealed that S100A11 may be mainly involved in the IL-17 signaling pathway. Finally, we discovered a correlation between S100A11 expression and immune cell infiltration on BC. S100A11 expression was positively associated with 17 immune checkpoint-related genes. In conclusion, this study indicates that S100A11 may contribute to a worse prognosis for BC and potentially has a significant impact through its influence on immune cell infiltration and the IL-17 signaling pathway

A Conjugate Gradient Algorithm with Function Value Information and N-Step Quadratic Convergence for Unconstrained Optimization

<div><p>It is generally acknowledged that the conjugate gradient (CG) method achieves global convergence—with at most a linear convergence rate—because CG formulas are generated by linear approximations of the objective functions. The quadratically convergent results are very limited. We introduce a new PRP method in which the restart strategy is also used. Moreover, the method we developed includes not only n-step quadratic convergence but also both the function value information and gradient value information. In this paper, we will show that the new PRP method (with either the Armijo line search or the Wolfe line search) is both linearly and quadratically convergent. The numerical experiments demonstrate that the new PRP algorithm is competitive with the normal CG method.</p></div

Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer&rsquo;s Diseases

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to construct functional connectivity (FC) in the brain for the diagnosis and analysis of brain disease. Current studies typically use the Pearson correlation coefficient to construct dynamic FC (dFC) networks, and then use this as a network metric to obtain the necessary features for brain disease diagnosis and analysis. This simple observational approach makes it difficult to extract potential high-level FC features from the representations, and also ignores the rich information on spatial and temporal variability in FC. In this paper, we construct the Latent Space Representation Network (LSRNet) and use two stages to train the network. In the first stage, an autoencoder is used to extract potential high-level features and inner connections in the dFC representations. In the second stage, high-level features are extracted using two perspective feature parses. Long Short-Term Memory (LSTM) networks are used to extract spatial and temporal features from the local perspective. Convolutional neural networks extract global high-level features from the global perspective. Finally, the fusion of spatial and temporal features with global high-level features is used to diagnose brain disease. In this paper, the proposed method is applied to the ANDI rs-fMRI dataset, and the classification accuracy reaches 84.6% for NC/eMCI, 95.1% for NC/AD, 80.6% for eMCI/lMCI, 84.2% for lMCI/AD and 57.3% for NC/eMCI/lMCI/AD. The experimental results show that the method has a good classification performance and provides a new approach to the diagnosis of other brain diseases

HPV-16, HPV-58, and HPV-33 are the most carcinogenic HPV genotypes in Southwestern China and their viral loads are associated with severity of premalignant lesions in the cervix

Abstract Background Currently, the role of human papillomavirus (HPV)-58 in southwestern China has been unexplored. Although there is some controversy, it is proposed that the viral load of HPV correlates with the severity of intraepithelial lesions. Methods We identified 7747 patients from south Sichuan and adjacent regions who were diagnosed with HPV between 2013 and 2017. The HR-HPV subtype distribution was analyzed and the patient’s viral loads were quantified using real-time RT-PCR. Results Among all 7747 patients screened for HPV genotypes, 1728 patients (22.31%) were identified as having HR-HPV subtypes. In patients without intraepithelial lesions (12.41%), HPV-52, HPV-16, and HPV-58 were the three most prevalent HR-HPV subtypes. Moreover, HPV-16, HPV-58, and HPV-33 were the most prevalent subtypes in patients with cervical intraepithelial neoplasia grade II (CINII) (42.86%) and grade III (CINIII) (59.81%), and accounted for the majority of invasive cervical cancer (ICC) (69.34%). Thus, viral loads of HPV-58, HPV-16, and HPV-33 positively correlated with the severity of cervical lesions (P < 0.001, P = 0.016, P = 0.026, respectively). Using receiver operating characteristic (ROC) curve analysis, the optimum thresholds for predicting severe intraepithelial lesions of cases (CINI, CINIII and ICC) with HPV-16, HPV-58, and HPV-33, respectively, were obtained, which were 1, 0.93, and 0.25, respectively. Conclusion In our study, we showed that HPV-16 was the most common carcinogenic HPV subtype in southwestern China followed by HPV-58 and HPV-33. Viral loads of these subtypes are associated with the severity of premalignant lesions in the cervix

The performance of Algorithm 4.1 and Algorithm N on NFG.

<p>The performance of Algorithm 4.1 and Algorithm N on NFG.</p

Fluorescent Hydrogen-Bonded Organic Framework for Sensing of Aromatic Compounds

A fluorescent hydrogen-bonded organic framework, HOF-1111, was designed and fabricated by using fluorescent tetraphenylethylene (TPE) as building block. The HOF-1111 showed high thermal stability and 3D structure. HOF-1111 can be used for sensing of aromatic compounds via a fluorescence quenching and enhancement mechanism